Between sample/beta diversity of Microbes by gut section
Calla Bush St George
2024-09-05
Goals
- Load in phyloseq data with rooted tree.
- Evaluate sequencing depth and remove sample.
- Normalize the read counts between samples.
- Calculate community dissimilarities. Numbers between 0 and 1. If 0,
completely similar versus if they are 1, then they are completely
dissimilar.
- Sorensen: Presence/ Absence. Weighted by number of shared taxa. Shared species as a binary-valye. Abundance-unweighted.
- Bray-Curtis: Relative abundance. Weighted by number of shared taxa. Shared abundant species: abundance weighted.
- (Abundance) Weighted UNIFRAC: Consider abundant species and where they fall on the tree.
- Visualize the community data with two unconstricted ordinations:
- PCoA: Linear method. Uses matrix algebra to calculate eigenvalye. Calculate how much variation is explained by each axis. Can choose to view axis 1, 2, 3, etc. and plot them together.
- NMDS: Non-linear method. Collapse multiple axes into two (or three) dimensions. Can see more axes of variation into fewer axes. Always need to report a stress value. (Ideally less than 0.15)
- Run statistics with PERMANOVA and betadispR.
Setup
Load Libraries
Load in colors
host_species_colors <- c(
"Acanthurus nigros" = "dodgerblue",
"Acanthurus achilles" = "royalblue",
"Acanthurus nigrofuscus" = "blue3",
"Acanthurus olivaceus" = "blue",
"Acanthurus triostegus" = "slateblue3",
"Ctenochaetus striatus" = "olivedrab",
"Naso brevirostris" = "red2",
"Naso lituratus" = "tomato4",
"Naso tonganus" = "salmon2",
"Naso unicornis" = "deeppink",
"Odax pullus" = "springgreen2",
"Zebrasoma flavescens" = "darkorange",
"Zebrasoma scopas" = "yellow2",
"Zebrasoma velifer" = "goldenrod")
region_colors <- c(
"Cook Islands" = "olivedrab4",
"Hawaii" = "deeppink",
GBR = "royalblue3",
Captivity = "brown")
gut_section_colors <- c(
III = "darkslateblue",
IV = "darkorchid1",
V = "cyan3",
feces = "cornflowerblue")
diet_colors <- c(
herbivore = "forestgreen",
detritovore = "hotpink4",
omnivore = "orange")Load in data
# Load in rooted phylogenetic tree!
load("data/07_Phylogenetic_Tree_epulos/phytree_preprocessed_physeq.RData")
unrooted_physeq## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1112 taxa and 83 samples ]
## sample_data() Sample Data: [ 83 samples by 10 sample variables ]
## tax_table() Taxonomy Table: [ 1112 taxa by 9 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1112 tips and 1110 internal nodes ]## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1112 taxa and 83 samples ]
## sample_data() Sample Data: [ 83 samples by 10 sample variables ]
## tax_table() Taxonomy Table: [ 1112 taxa by 9 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1112 tips and 1111 internal nodes ]Explore Read Counts
Raw Read Depth
# Sequence depth will inform us on how we want to normalize our data
# Calculate the total number of reads per sample
raw_total_seqs_df <-
unrooted_physeq %>%
# Calculate the sample read sums
sample_sums() %>%
data.frame()
# Name the column
colnames(raw_total_seqs_df)[1] <- "TotalSeqs"
head(raw_total_seqs_df)## TotalSeqs
## A16 15903
## A17 20099
## A18 1464
## AA1 2920
## AA2 9125
## AA3 10225Remove lowly sequenced sample
Normalize Read Counts
### scale_reads function and matround function
####################################################################################
# Function to scale reads: http://deneflab.github.io/MicrobeMiseq/
# Scales reads by
# 1) taking proportions
# 2) multiplying by a given library size of n
# 3) rounding
# Default for n is the minimum sample size in your library
# Default for round is floor
matround <- function(x){trunc(x+0.5)}
scale_reads <- function(physeq, n = min(sample_sums(physeq)), round = "round") {
# transform counts to n
physeq.scale <- transform_sample_counts(physeq, function(x) {(n * x/sum(x))})
# Pick the rounding functions
if (round == "floor"){
otu_table(physeq.scale) <- floor(otu_table(physeq.scale))
} else if (round == "round"){
otu_table(physeq.scale) <- round(otu_table(physeq.scale))
} else if (round == "matround"){
otu_table(physeq.scale) <- matround(otu_table(physeq.scale))
}
# Prune taxa and return new phyloseq object
physeq.scale <- prune_taxa(taxa_sums(physeq.scale) > 0, physeq.scale)
return(physeq.scale)
}Rescale all reads so they all represent the count of the lowest number of sequence reads. We will expect each sample to have # of reads around 2200
This is where one might decide to use rarefaction to normalize the data.
Scale reads and check the distribution of the seq depth
## [1] 559# Scale reads by the above function
scaled_rooted_physeq <-
midroot_physeq %>%
scale_reads(round = "matround")
# Calculate read depth
## Look at total number of sequences in each sample and compare to what we had before
scaled_total_seqs_df <-
scaled_rooted_physeq %>%
sample_sums() %>%
data.frame()
head(scaled_total_seqs_df)## .
## A16 555
## A17 562
## A18 561
## AA1 559
## AA2 556
## AA3 554# Change first column name to be "TotalSeqs"
colnames(scaled_total_seqs_df)[1] <- "TotalSeqs"
# Inspect
head(scaled_total_seqs_df)## TotalSeqs
## A16 555
## A17 562
## A18 561
## AA1 559
## AA2 556
## AA3 554# Check range of data
min_seqs <-
min(scaled_total_seqs_df)
max_seqs <-
max(scaled_total_seqs_df)
# Range of seqs
max_seqs - min_seqs## [1] 11# Plot histogram
scaled_total_seqs_df %>%
ggplot(aes(x = TotalSeqs)) +
geom_histogram(bins = 50) +
scale_x_continuous(limits = c(0, 600)) +
labs(title = "Scaled Sequencing Depth at 555") +
theme_bw()## Warning: Removed 2 rows containing missing values (`geom_bar()`).
## TotalSeqs
## A16 555
## A17 562
## A18 561
## AA1 559
## AA2 556
## AA3 554Calculate & Visualize Community Dissimiliarity
Exploratory analyses from the Paily & Shankar (2016) paper, which is using unconstrained ordination methods like PCoA.
Sorenson PCoA
# Calculate sorenson dissimularity: Abundance-unweighted of shared taxa
scaled_soren_pcoa <-
ordinate(
physeq = scaled_rooted_physeq,
method = "PCoA",
distance = "bray", binary = TRUE)
#str(scaled_soren_pcoa)
# Plot the ordination host species
soren_diet_pcoa <- plot_ordination(
physeq = scaled_rooted_physeq,
ordination = scaled_soren_pcoa,
color = "diet",
title = "Sorensen PCoA") +
geom_point(size=5, alpha = 0.5, aes(color = diet)) +
scale_color_manual(values = diet_colors) +
theme_bw()
# Show the plot
soren_diet_pcoa
Bray-Curtis PCoA
# Calculate the BC distance
scaled_BC_pcoa <-
ordinate(
physeq = scaled_rooted_physeq,
method = "PCoA",
distance = "bray")
# Plot the PCoA
bray_diet_pcoa <-
plot_ordination(
physeq = scaled_rooted_physeq,
ordination = scaled_BC_pcoa,
color = "diet",
title = "Bray-Curtis PCoA") +
geom_point(size=5, alpha = 0.5, aes(color = diet)) +
scale_color_manual(values = c(diet_colors)) +
theme_bw()
bray_diet_pcoa
Weighted-Unifrac PCoA
# Calculate the BC distance
scaled_wUNI_pcoa <-
ordinate(
physeq = scaled_rooted_physeq,
method = "PCoA",
distance = "wunifrac")
# Plot the PCoA
wUNI_diet_pcoa <-
plot_ordination(
physeq = scaled_rooted_physeq,
ordination = scaled_wUNI_pcoa,
color = "diet",
title = "Weighted Unifrac PCoA") +
geom_point(size=5, alpha = 0.5, aes(color = diet)) +
scale_color_manual(values = c(diet_colors)) +
theme_bw()
wUNI_diet_pcoa
Combine PCoAs
Let’s plot all three together into one plot to have a concise visualization of the three metrics.
(soren_diet_pcoa + theme(legend.position = "none")) +
(bray_diet_pcoa + theme(legend.position = "none")) +
(wUNI_diet_pcoa + theme(legend.position = "none"))
NMDS
Weighted Unifrac
Since we did 3 of the dissimilarity metrics for the PCoA, let’s just plot one example of them for the NMDS plotting. Here, we will use weighted Unifrac
# Calculate the Weighted Unifrac distance
scaled_wUNI_nmds <-
ordinate(
physeq = scaled_rooted_physeq,
method = "NMDS",
distance = "wunifrac")## Run 0 stress 0.1304584
## Run 1 stress 0.1550611
## Run 2 stress 0.1322358
## Run 3 stress 0.1861179
## Run 4 stress 0.1811754
## Run 5 stress 0.1415828
## Run 6 stress 0.1864384
## Run 7 stress 0.1467709
## Run 8 stress 0.1653355
## Run 9 stress 0.1604274
## Run 10 stress 0.1566682
## Run 11 stress 0.155879
## Run 12 stress 0.1659982
## Run 13 stress 0.1539487
## Run 14 stress 0.166497
## Run 15 stress 0.1449272
## Run 16 stress 0.1805283
## Run 17 stress 0.163302
## Run 18 stress 0.1322358
## Run 19 stress 0.1748714
## Run 20 stress 0.1372046
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 14: stress ratio > sratmax
## 6: scale factor of the gradient < sfgrmin# Plot the PCoA
wUNI_host_species_nmds <-
plot_ordination(
physeq = scaled_rooted_physeq,
ordination = scaled_wUNI_nmds,
color = "host_species") +
geom_point(size=5, alpha = 0.5, aes(color = host_species)) +
scale_color_manual(values = c(host_species_colors)) +
theme_bw() + labs(color = "host_species")
wUNI_host_species_nmds
wUNI_gut_section_nmds <-
plot_ordination(
physeq = scaled_rooted_physeq,
ordination = scaled_wUNI_nmds,
color = "gut_section") +
geom_point(size=5, alpha = 0.5, aes(color = gut_section)) +
scale_color_manual(values = c(gut_section_colors)) +
theme_bw() + labs(color = "gut_section")
wUNI_gut_section_nmds
wUNI_region_nmds <-
plot_ordination(
physeq = scaled_rooted_physeq,
ordination = scaled_wUNI_nmds,
color = "region") +
geom_point(size=5, alpha = 0.5, aes(color = region)) +
scale_color_manual(values = c(region_colors)) +
theme_bw() + labs(color = "region")
wUNI_region_nmds
wUNI_diet_nmds <-
plot_ordination(
physeq = scaled_rooted_physeq,
ordination = scaled_wUNI_nmds,
color = "diet") +
geom_point(size=5, alpha = 0.5, aes(color = diet)) +
scale_color_manual(values = c(diet_colors)) +
theme_bw() + labs(color = "diet")
wUNI_diet_nmds
wUNI_diet_gut_section_nmds <-
plot_ordination(
physeq = scaled_rooted_physeq,
ordination = scaled_wUNI_nmds,
color = "diet",
shape = "gut_section") +
geom_point(size=5, alpha = 0.5, aes(color = diet)) +
scale_shape_manual(values = c(0,2,15,16,17,18)) +
scale_color_manual(values = c(diet_colors)) +
theme_bw() + labs(color = "diet")
wUNI_diet_gut_section_nmds
wUNI_gut_section_diet_nmds <-
plot_ordination(
physeq = scaled_rooted_physeq,
ordination = scaled_wUNI_nmds,
color = "gut_section",
shape = "diet") +
geom_point(size=5, alpha = 0.5, aes(color = gut_section)) +
scale_shape_manual(values = c(15,16,17)) +
scale_color_manual(values = c(gut_section_colors)) +
theme_bw() + labs(color = "gut_section")
wUNI_gut_section_diet_nmds
wUNI_diet_region_nmds <-
plot_ordination(
physeq = scaled_rooted_physeq,
ordination = scaled_wUNI_nmds,
color = "diet",
shape = "region") +
geom_point(size=5, alpha = 0.5, aes(color = diet)) +
scale_shape_manual(values = c(15,16,17,18,19)) +
scale_color_manual(values = c(diet_colors)) +
theme_bw() + labs(color = "diet")
wUNI_diet_region_nmds
wUNI_region_diet_nmds <-
plot_ordination(
physeq = scaled_rooted_physeq,
ordination = scaled_wUNI_nmds,
color = "region",
shape = "diet") +
geom_point(size=5, alpha = 0.5, aes(color = region)) +
scale_shape_manual(values = c(15,16,17,18)) +
scale_color_manual(values = c(region_colors)) +
theme_bw() + labs(color = "region")
wUNI_region_diet_nmds
wUNI_host_species_diet_nmds <-
plot_ordination(
physeq = scaled_rooted_physeq,
ordination = scaled_wUNI_nmds,
color = "host_species",
shape = "diet") +
geom_point(size=5, alpha = 0.5, aes(color = host_species)) +
scale_shape_manual(values = c(15, 16, 17, 18)) +
scale_color_manual(values = c(host_species_colors)) +
theme_bw() + labs(color = "host_species")
wUNI_host_species_diet_nmds
Statistical Significance Testing
PERMANOVA
# Calculate all three of the distance matrices
scaled_sorensen_dist <- phyloseq::distance(scaled_rooted_physeq, method = "bray", binary = TRUE)
scaled_bray_dist <- phyloseq::distance(scaled_rooted_physeq, method = "bray")
scaled_wUnifrac_dist <- phyloseq::distance(scaled_rooted_physeq, method = "wunifrac")
# make a data frame from the sample_data
# All distance matrices will be the same metadata because they
# originate from the same phyloseq object.
metadata <- data.frame(sample_data(scaled_rooted_physeq))
# Adonis test
# In this example we are testing the hypothesis that the five stations
# that were collected have different centroids in the ordination space
# for each of the dissimilarity metrics, we are using a discrete variable
# diet
adonis2(scaled_sorensen_dist ~ diet, data = metadata)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_sorensen_dist ~ diet, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## diet 2 3.3465 0.15627 7.4086 0.001 ***
## Residual 80 18.0683 0.84373
## Total 82 21.4148 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_bray_dist ~ diet, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## diet 2 4.4627 0.15375 7.2671 0.001 ***
## Residual 80 24.5640 0.84625
## Total 82 29.0268 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_wUnifrac_dist ~ diet, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## diet 2 2.6494 0.16349 7.8178 0.001 ***
## Residual 80 13.5557 0.83651
## Total 82 16.2051 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_sorensen_dist ~ region, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## region 3 2.0363 0.09509 2.7671 0.001 ***
## Residual 79 19.3785 0.90491
## Total 82 21.4148 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_bray_dist ~ region, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## region 3 2.405 0.08286 2.379 0.001 ***
## Residual 79 26.622 0.91714
## Total 82 29.027 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_wUnifrac_dist ~ region, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## region 3 1.5085 0.09309 2.703 0.002 **
## Residual 79 14.6965 0.90691
## Total 82 16.2051 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_sorensen_dist ~ gut_section, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## gut_section 5 1.2577 0.05873 0.9609 0.561
## Residual 77 20.1571 0.94127
## Total 82 21.4148 1.00000## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_bray_dist ~ gut_section, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## gut_section 5 1.7897 0.06166 1.0119 0.434
## Residual 77 27.2371 0.93834
## Total 82 29.0268 1.00000## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_wUnifrac_dist ~ gut_section, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## gut_section 5 0.7348 0.04535 0.7315 0.807
## Residual 77 15.4702 0.95465
## Total 82 16.2051 1.00000## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_sorensen_dist ~ host_species, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## host_species 13 11.117 0.51911 5.7295 0.001 ***
## Residual 69 10.298 0.48089
## Total 82 21.415 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_bray_dist ~ host_species, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## host_species 13 15.140 0.5216 5.787 0.001 ***
## Residual 69 13.886 0.4784
## Total 82 29.027 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_wUnifrac_dist ~ host_species, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## host_species 13 8.5083 0.52504 5.8673 0.001 ***
## Residual 69 7.6968 0.47496
## Total 82 16.2051 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Note that:
- R2 = the percent variation explained.
- F = the F-Statistic, which represents the importance value.
- Pr(>F) = the pvalue
# We might also care about other variables
# Here, we will add date and fraction as variables
# multiplicative model ORDER MATTERS!
adonis2(scaled_wUnifrac_dist ~ diet * region, data = metadata)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_wUnifrac_dist ~ diet * region, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## diet 2 2.6494 0.16349 8.7188 0.001 ***
## region 3 1.3516 0.08341 2.9654 0.001 ***
## diet:region 2 0.8090 0.04992 2.6622 0.012 *
## Residual 75 11.3951 0.70318
## Total 82 16.2051 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_wUnifrac_dist ~ diet * gut_section, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## diet 2 2.6494 0.16349 7.7321 0.001 ***
## gut_section 5 0.6696 0.04132 0.7817 0.761
## diet:gut_section 5 0.8935 0.05514 1.0430 0.390
## Residual 70 11.9926 0.74005
## Total 82 16.2051 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = scaled_wUnifrac_dist ~ diet * host_species, data = metadata)
## Df SumOfSqs R2 F Pr(>F)
## diet 2 2.6494 0.16349 11.8755 0.001 ***
## host_species 11 5.8589 0.36155 4.7749 0.001 ***
## Residual 69 7.6968 0.47496
## Total 82 16.2051 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1We can also run tests that include additive (+) or multipliciatve models, which include the interaction term between variables.
BetaDispR
The PERMANOVA is sensitive to variance/dispersion in the data. Therefore, we need to run a homogeneity of dispersion test to test for the sensitivity of our PERMANOVA results to variance.
# Homogeneity of Disperson test with beta dispr
# Sorensen
beta_soren_diet <- betadisper(scaled_sorensen_dist, metadata$diet)
permutest(beta_soren_diet)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.35638 0.178190 13.594 999 0.001 ***
## Residuals 80 1.04867 0.013108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Bray-curtis
beta_bray_diet <- betadisper(scaled_bray_dist, metadata$diet)
permutest(beta_bray_diet)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.67166 0.33583 34.258 999 0.001 ***
## Residuals 80 0.78425 0.00980
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Weighted Unifrac
weighted_unifrac_diet <- betadisper(scaled_wUnifrac_dist, metadata$diet)
permutest(beta_bray_diet)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.67166 0.33583 34.258 999 0.001 ***
## Residuals 80 0.78425 0.00980
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Session Information
For Reproducibility
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.3.2 (2023-10-31)
## os macOS Sonoma 14.6.1
## system x86_64, darwin20
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2024-09-05
## pandoc 3.1.11 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/x86_64/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [2] CRAN (R 4.3.0)
## ade4 1.7-22 2023-02-06 [2] CRAN (R 4.3.0)
## ape 5.7-1 2023-03-13 [2] CRAN (R 4.3.0)
## backports 1.4.1 2021-12-13 [2] CRAN (R 4.3.0)
## Biobase 2.62.0 2023-10-24 [2] Bioconductor
## BiocGenerics 0.48.1 2023-11-01 [2] Bioconductor
## biomformat 1.30.0 2023-10-24 [2] Bioconductor
## Biostrings 2.70.2 2024-01-28 [2] Bioconductor 3.18 (R 4.3.2)
## bitops 1.0-7 2021-04-24 [2] CRAN (R 4.3.0)
## broom 1.0.5 2023-06-09 [2] CRAN (R 4.3.0)
## bslib 0.6.1 2023-11-28 [2] CRAN (R 4.3.0)
## cachem 1.0.8 2023-05-01 [2] CRAN (R 4.3.0)
## car 3.1-2 2023-03-30 [1] CRAN (R 4.3.0)
## carData 3.0-5 2022-01-06 [1] CRAN (R 4.3.0)
## cli 3.6.2 2023-12-11 [2] CRAN (R 4.3.0)
## cluster 2.1.6 2023-12-01 [2] CRAN (R 4.3.0)
## codetools 0.2-19 2023-02-01 [2] CRAN (R 4.3.2)
## colorspace 2.1-0 2023-01-23 [2] CRAN (R 4.3.0)
## crayon 1.5.2 2022-09-29 [2] CRAN (R 4.3.0)
## data.table 1.15.0 2024-01-30 [2] CRAN (R 4.3.2)
## devtools * 2.4.5 2022-10-11 [2] CRAN (R 4.3.0)
## digest 0.6.34 2024-01-11 [2] CRAN (R 4.3.0)
## dplyr * 1.1.4 2023-11-17 [2] CRAN (R 4.3.0)
## ellipsis 0.3.2 2021-04-29 [2] CRAN (R 4.3.0)
## evaluate 0.23 2023-11-01 [2] CRAN (R 4.3.0)
## fansi 1.0.6 2023-12-08 [2] CRAN (R 4.3.0)
## farver 2.1.1 2022-07-06 [2] CRAN (R 4.3.0)
## fastmap 1.1.1 2023-02-24 [2] CRAN (R 4.3.0)
## forcats * 1.0.0 2023-01-29 [2] CRAN (R 4.3.0)
## foreach 1.5.2 2022-02-02 [2] CRAN (R 4.3.0)
## fs 1.6.3 2023-07-20 [2] CRAN (R 4.3.0)
## generics 0.1.3 2022-07-05 [2] CRAN (R 4.3.0)
## GenomeInfoDb 1.38.6 2024-02-08 [2] Bioconductor 3.18 (R 4.3.2)
## GenomeInfoDbData 1.2.11 2023-11-13 [2] Bioconductor
## ggplot2 * 3.4.4 2023-10-12 [1] CRAN (R 4.3.0)
## ggpubr * 0.6.0.999 2024-07-30 [1] Github (kassambara/ggpubr@6aeb4f7)
## ggsignif 0.6.4 2022-10-13 [1] CRAN (R 4.3.0)
## glue 1.7.0 2024-01-09 [1] CRAN (R 4.3.0)
## gtable 0.3.4 2023-08-21 [2] CRAN (R 4.3.0)
## highr 0.10 2022-12-22 [2] CRAN (R 4.3.0)
## hms 1.1.3 2023-03-21 [2] CRAN (R 4.3.0)
## htmltools 0.5.7 2023-11-03 [2] CRAN (R 4.3.0)
## htmlwidgets 1.6.4 2023-12-06 [2] CRAN (R 4.3.0)
## httpuv 1.6.14 2024-01-26 [2] CRAN (R 4.3.2)
## igraph 2.0.1.1 2024-01-30 [2] CRAN (R 4.3.2)
## IRanges 2.36.0 2023-10-24 [2] Bioconductor
## iterators 1.0.14 2022-02-05 [2] CRAN (R 4.3.0)
## jquerylib 0.1.4 2021-04-26 [2] CRAN (R 4.3.0)
## jsonlite 1.8.8 2023-12-04 [2] CRAN (R 4.3.0)
## knitr 1.45 2023-10-30 [2] CRAN (R 4.3.0)
## labeling 0.4.3 2023-08-29 [2] CRAN (R 4.3.0)
## later 1.3.2 2023-12-06 [2] CRAN (R 4.3.0)
## lattice * 0.22-5 2023-10-24 [2] CRAN (R 4.3.0)
## lifecycle 1.0.4 2023-11-07 [2] CRAN (R 4.3.0)
## lubridate * 1.9.3 2023-09-27 [2] CRAN (R 4.3.0)
## magrittr 2.0.3 2022-03-30 [2] CRAN (R 4.3.0)
## MASS 7.3-60.0.1 2024-01-13 [2] CRAN (R 4.3.0)
## Matrix 1.6-5 2024-01-11 [2] CRAN (R 4.3.0)
## memoise 2.0.1 2021-11-26 [2] CRAN (R 4.3.0)
## mgcv 1.9-1 2023-12-21 [2] CRAN (R 4.3.0)
## mime 0.12 2021-09-28 [2] CRAN (R 4.3.0)
## miniUI 0.1.1.1 2018-05-18 [2] CRAN (R 4.3.0)
## multtest 2.58.0 2023-10-24 [2] Bioconductor
## munsell 0.5.0 2018-06-12 [2] CRAN (R 4.3.0)
## nlme 3.1-164 2023-11-27 [2] CRAN (R 4.3.0)
## pacman 0.5.1 2019-03-11 [1] CRAN (R 4.3.0)
## patchwork * 1.2.0.9000 2024-05-07 [1] Github (thomasp85/patchwork@d943757)
## permute * 0.9-7 2022-01-27 [2] CRAN (R 4.3.0)
## phyloseq * 1.46.0 2023-10-24 [2] Bioconductor
## pillar 1.9.0 2023-03-22 [2] CRAN (R 4.3.0)
## pkgbuild 1.4.3 2023-12-10 [2] CRAN (R 4.3.0)
## pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.3.0)
## pkgload 1.3.4 2024-01-16 [2] CRAN (R 4.3.0)
## plyr 1.8.9 2023-10-02 [1] CRAN (R 4.3.0)
## profvis 0.3.8 2023-05-02 [2] CRAN (R 4.3.0)
## promises 1.2.1 2023-08-10 [2] CRAN (R 4.3.0)
## purrr * 1.0.2 2023-08-10 [2] CRAN (R 4.3.0)
## R6 2.5.1 2021-08-19 [2] CRAN (R 4.3.0)
## Rcpp 1.0.12 2024-01-09 [2] CRAN (R 4.3.0)
## RCurl 1.98-1.14 2024-01-09 [2] CRAN (R 4.3.0)
## readr * 2.1.5 2024-01-10 [1] CRAN (R 4.3.0)
## remotes 2.4.2.1 2023-07-18 [2] CRAN (R 4.3.0)
## reshape2 1.4.4 2020-04-09 [2] CRAN (R 4.3.0)
## rhdf5 2.46.1 2023-11-29 [2] Bioconductor
## rhdf5filters 1.14.1 2023-11-06 [2] Bioconductor
## Rhdf5lib 1.24.2 2024-02-07 [2] Bioconductor 3.18 (R 4.3.2)
## rlang 1.1.3 2024-01-10 [2] CRAN (R 4.3.0)
## rmarkdown 2.25 2023-09-18 [2] CRAN (R 4.3.0)
## rstatix * 0.7.2 2023-02-01 [1] CRAN (R 4.3.0)
## rstudioapi 0.15.0 2023-07-07 [2] CRAN (R 4.3.0)
## S4Vectors 0.40.2 2023-11-23 [2] Bioconductor
## sass 0.4.8 2023-12-06 [2] CRAN (R 4.3.0)
## scales 1.3.0 2023-11-28 [2] CRAN (R 4.3.0)
## sessioninfo 1.2.2 2021-12-06 [2] CRAN (R 4.3.0)
## shiny 1.8.0 2023-11-17 [2] CRAN (R 4.3.0)
## stringi 1.8.3 2023-12-11 [2] CRAN (R 4.3.0)
## stringr * 1.5.1 2023-11-14 [2] CRAN (R 4.3.0)
## survival 3.5-7 2023-08-14 [2] CRAN (R 4.3.0)
## tibble * 3.2.1 2023-03-20 [2] CRAN (R 4.3.0)
## tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.3.2)
## tidyselect 1.2.0 2022-10-10 [2] CRAN (R 4.3.0)
## tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.3.0)
## timechange 0.3.0 2024-01-18 [2] CRAN (R 4.3.0)
## tzdb 0.4.0 2023-05-12 [2] CRAN (R 4.3.0)
## urlchecker 1.0.1 2021-11-30 [2] CRAN (R 4.3.0)
## usethis * 2.2.2 2023-07-06 [2] CRAN (R 4.3.0)
## utf8 1.2.4 2023-10-22 [2] CRAN (R 4.3.0)
## vctrs 0.6.5 2023-12-01 [2] CRAN (R 4.3.0)
## vegan * 2.6-4 2022-10-11 [1] CRAN (R 4.3.0)
## withr 3.0.0 2024-01-16 [2] CRAN (R 4.3.0)
## xfun 0.42 2024-02-08 [2] CRAN (R 4.3.2)
## xtable 1.8-4 2019-04-21 [2] CRAN (R 4.3.0)
## XVector 0.42.0 2023-10-24 [2] Bioconductor
## yaml 2.3.8 2023-12-11 [2] CRAN (R 4.3.0)
## zlibbioc 1.48.0 2023-10-24 [2] Bioconductor
##
## [1] /Users/cab565/Library/R/x86_64/4.3/library
## [2] /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/library
##
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